Predicting noise-induced hearing loss with machine learning: the influence of tinnitus as a predictive factor

Abstract Objectives This study aimed to determine which machine learning model is most suitable for predicting noise-induced hearing loss and the effect of tinnitus on the models’ accuracy. Methods Two hundred workers employed in a metal industry were selected for this study and tested using pure tone audiometry. Their occupational exposure histories were collected, analysed and used to create a dataset. Eighty per cent of the data collected was used to train six machine learning models and the remaining 20 per cent was used to test the models. Results Eight workers (40.5 per cent) had bilaterally normal hearing and 119 (59.5 per cent) had hearing loss. Tinnitus was the second most important indicator after age for noise-induced hearing loss. The support vector machine was the best-performing algorithm, with 90 per cent accuracy, 91 per cent F1 score, 95 per cent precision and 88 per cent recall. Conclusion The use of tinnitus as a risk factor in the support vector machine model may increase the success of occupational health and safety programmes.